wizardcoder-15b-v1.0
Maintainer: lucataco
2
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
The wizardcoder-15b-v1.0 is a large language model created by the Replicate user lucataco. It is a variant of the WizardLM family of models, which have shown impressive performance on tasks like code generation. While not much is known about the specific architecture or training process of this particular model, it is likely a powerful tool for a variety of natural language processing tasks.
When compared to similar models like the wizardcoder-34b-v1.0, wizard-mega-13b-awq, wizardlm-2-8x22b, and WizardLM-13B-V1.0, the wizardcoder-15b-v1.0 appears to be a more compact and efficient version, while still maintaining strong capabilities. Its potential use cases and performance characteristics are not entirely clear from the available information.
Model inputs and outputs
Inputs
- prompt: A text prompt that the model will use to generate a response.
- max_new_tokens: The maximum number of new tokens the model will generate in response to the prompt.
- temperature: A value that controls the randomness of the model's output, with lower values resulting in more focused and deterministic responses.
Outputs
- output: The text generated by the model in response to the input prompt.
- id: A unique identifier for the model run.
- version: The version of the model used.
- created_at: The timestamp when the model run was initiated.
- started_at: The timestamp when the model run started.
- completed_at: The timestamp when the model run completed.
- logs: The logs from the model run.
- error: Any errors that occurred during the model run.
- status: The status of the model run (e.g., "succeeded", "failed").
- metrics: Performance metrics for the model run, such as the prediction time.
Capabilities
The wizardcoder-15b-v1.0 model appears to be a capable code generation tool, as demonstrated by the example of generating a Python function to check if a number is prime. Its ability to produce coherent and relevant code snippets suggests it could be useful for tasks like software development, data analysis, and automation.
What can I use it for?
The wizardcoder-15b-v1.0 model could be a valuable tool for developers and data scientists looking to automate or streamline various tasks. For example, it could be integrated into an IDE to assist with code completion and generation, or used to generate boilerplate code for common programming tasks. Additionally, it could be employed in data analysis workflows to generate custom scripts and functions on demand.
Things to try
One interesting thing to try with the wizardcoder-15b-v1.0 model would be to explore its capabilities in generating more complex code, such as multi-function programs or algorithms that solve specific problems. It would also be worthwhile to experiment with different prompting strategies and temperature settings to see how they affect the model's outputs and performance.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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